Machine learning for data-driven diagnosis has been actively studied in medicine to provide better healthcare.Supporting analysis of a patient cohort similar to a patient under treatment is a key task for clinicians t...Machine learning for data-driven diagnosis has been actively studied in medicine to provide better healthcare.Supporting analysis of a patient cohort similar to a patient under treatment is a key task for clinicians to make decisions with high confidence.However,such analysis is not straightforward due to the characteristics of medical records:high dimensionality,irregularity in time,and sparsity.To address this challenge,we introduce a method for similarity calculation of medical records.Our method employs event and sequence embeddings.While we use an autoencoder for the event embedding,we apply its variant with the self-attention mechanism for the sequence embedding.Moreover,in order to better handle the irregularity of data,we enhance the self-attention mechanism with consideration of different time intervals.We have developed a visual analytics system to support comparative studies of patient records.To make a comparison of sequences with different lengths easier,our system incorporates a sequence alignment method.Through its interactive interface,the user can quickly identify patients of interest and conveniently review both the temporal and multivariate aspects of the patient records.We demonstrate the effectiveness of our design and system with case studies using a real-world dataset from the neonatal intensive care unit of UC Davis.展开更多
Large,complex networks are commonly found in many application domains,such as sociology,biology,and software engineering.Analyzing such networks can be a non-trivial task,as it often takes many interactions to derive ...Large,complex networks are commonly found in many application domains,such as sociology,biology,and software engineering.Analyzing such networks can be a non-trivial task,as it often takes many interactions to derive a finding.It is thus beneficial to capture and summarize the important steps in an analysis.This provenance would then effectively support recalling,reusing,reproducing,and sharing the analysis process and results.However,the provenance of analyzing a large,complex network would often be a long interaction record.To automatically compose a concise visual summarization of network analysis provenance,we introduce a ranking model together with a reduction algorithm.The model identifies and orders important interactions used in the network analysis.Based on this model,our algorithm is able to minimize the provenance,while still preserving all the essential steps for recalling and sharing the analysis process and results.We create a prototype system demonstrating the effectiveness of our model and algorithm with two usage scenarios.展开更多
There is a growing trend of applying machine learning methods to medical datasets in order to predict patients’future status.Although some of these methods achieve high performance,challenges still exist in comparing...There is a growing trend of applying machine learning methods to medical datasets in order to predict patients’future status.Although some of these methods achieve high performance,challenges still exist in comparing and evaluating different models through their interpretable information.Such analytics can help clinicians improve evidence-based medical decision making.In this work,we develop a visual analytics system that compares multiple models’prediction criteria and evaluates their consistency.With our system,users can generate knowledge on different models’inner criteria and how confidently we can rely on each model’s prediction for a certain patient.Through a case study of a publicly available clinical dataset,we demonstrate the effectiveness of our visual analytics system to assist clinicians and researchers in comparing and quantitatively evaluating different machine learning methods.展开更多
The overall efficiency of an extreme-scale supercomputer largely relies on the performance of its network interconnects.Several of the state of the art supercomputers use networks based on the increasingly popular Dra...The overall efficiency of an extreme-scale supercomputer largely relies on the performance of its network interconnects.Several of the state of the art supercomputers use networks based on the increasingly popular Dragonfly topology.It is crucial to study the behavior and performance of different parallel applications running on Dragonfly networks in order to make optimal system configurations and design choices,such as job scheduling and routing strategies.However,in order to study these temporal network behavior,we would need a tool to analyze and correlate numerous sets of multivariate time-series data collected from the Dragonfly's multi-level hierarchies.This paper presents such a tool-a visual analytics system-that uses the Dragonfly network to investigate the temporal behavior and optimize the communication performance of a supercomputer.We coupled interactive visualization with time-series analysis methods to help reveal hidden patterns in the network behavior with respect to different parallel applications and system configurations.Our system also provides multiple coordinated views for connecting behaviors observed at different levels of the network hierarchies,which effectively helps visual analysis tasks.We demonstrate the effectiveness of the system with a set of case studies.Our system and findings can not only help improve the communication performance of supercomputing applications,but also the network performance of next-generation supercomputers.展开更多
基金the U.S.National Science Foundation through grant IIS-1741536 and a 2019 Seed Fund Award from CITRIS and the Banatao Institute at the University of California,United States.
文摘Machine learning for data-driven diagnosis has been actively studied in medicine to provide better healthcare.Supporting analysis of a patient cohort similar to a patient under treatment is a key task for clinicians to make decisions with high confidence.However,such analysis is not straightforward due to the characteristics of medical records:high dimensionality,irregularity in time,and sparsity.To address this challenge,we introduce a method for similarity calculation of medical records.Our method employs event and sequence embeddings.While we use an autoencoder for the event embedding,we apply its variant with the self-attention mechanism for the sequence embedding.Moreover,in order to better handle the irregularity of data,we enhance the self-attention mechanism with consideration of different time intervals.We have developed a visual analytics system to support comparative studies of patient records.To make a comparison of sequences with different lengths easier,our system incorporates a sequence alignment method.Through its interactive interface,the user can quickly identify patients of interest and conveniently review both the temporal and multivariate aspects of the patient records.We demonstrate the effectiveness of our design and system with case studies using a real-world dataset from the neonatal intensive care unit of UC Davis.
基金This research is sponsored in part by the U.S.National Science Foundation through grants IIS-1528203 and IIS-1741536.
文摘Large,complex networks are commonly found in many application domains,such as sociology,biology,and software engineering.Analyzing such networks can be a non-trivial task,as it often takes many interactions to derive a finding.It is thus beneficial to capture and summarize the important steps in an analysis.This provenance would then effectively support recalling,reusing,reproducing,and sharing the analysis process and results.However,the provenance of analyzing a large,complex network would often be a long interaction record.To automatically compose a concise visual summarization of network analysis provenance,we introduce a ranking model together with a reduction algorithm.The model identifies and orders important interactions used in the network analysis.Based on this model,our algorithm is able to minimize the provenance,while still preserving all the essential steps for recalling and sharing the analysis process and results.We create a prototype system demonstrating the effectiveness of our model and algorithm with two usage scenarios.
基金the U.S.National Science Foundation through grant IIS-1741536 and a 2019 Seed Fund Award from CITRIS and the Banatao Institute at the University of California.
文摘There is a growing trend of applying machine learning methods to medical datasets in order to predict patients’future status.Although some of these methods achieve high performance,challenges still exist in comparing and evaluating different models through their interpretable information.Such analytics can help clinicians improve evidence-based medical decision making.In this work,we develop a visual analytics system that compares multiple models’prediction criteria and evaluates their consistency.With our system,users can generate knowledge on different models’inner criteria and how confidently we can rely on each model’s prediction for a certain patient.Through a case study of a publicly available clinical dataset,we demonstrate the effectiveness of our visual analytics system to assist clinicians and researchers in comparing and quantitatively evaluating different machine learning methods.
基金This research was sponsored by the Advanced Scientific Computing Research Program,the Office of Science,U.SDepartment of Energy through grants DE-SC0014917,DE-SC0012610,and DE-AC02-06CH11357.
文摘The overall efficiency of an extreme-scale supercomputer largely relies on the performance of its network interconnects.Several of the state of the art supercomputers use networks based on the increasingly popular Dragonfly topology.It is crucial to study the behavior and performance of different parallel applications running on Dragonfly networks in order to make optimal system configurations and design choices,such as job scheduling and routing strategies.However,in order to study these temporal network behavior,we would need a tool to analyze and correlate numerous sets of multivariate time-series data collected from the Dragonfly's multi-level hierarchies.This paper presents such a tool-a visual analytics system-that uses the Dragonfly network to investigate the temporal behavior and optimize the communication performance of a supercomputer.We coupled interactive visualization with time-series analysis methods to help reveal hidden patterns in the network behavior with respect to different parallel applications and system configurations.Our system also provides multiple coordinated views for connecting behaviors observed at different levels of the network hierarchies,which effectively helps visual analysis tasks.We demonstrate the effectiveness of the system with a set of case studies.Our system and findings can not only help improve the communication performance of supercomputing applications,but also the network performance of next-generation supercomputers.
